- name
- supermemory
- version
- 0.2.1
- description
- Long-term agent memory with atomic fact extraction, relational versioning, semantic search, and entity profiles. Extracts facts from conversations, tracks how knowledge changes over time (updates/contradicts/extends), and provides instant recall across sessions and agents. Local-first (SQLite + on-device embeddings).
- tags
- memory, knowledge-graph, semantic-search, multi-agent, local-first
Supermemory
Long-term memory for AI agents. Extracts atomic facts from text, tracks relations between memories (updates, contradicts, extends), embeds locally for semantic search, and auto-builds entity profiles.
Setup
pip install openclaw-supermemory[local]
supermemory init # creates ~/.supermemory/memory.db
supermemory serve # starts API on :8642Requires an LLM API key for fact extraction (default: Anthropic Haiku).
export ANTHROPIC_API_KEY=sk-...
# or configure via ~/.supermemory/config.yamlCommands
Ingest (extract facts from text)
supermemory ingest "The project deadline moved to April 15. Sarah replaced Tom as lead." \
--session meeting-notes --agent kitLLM extracts atomic facts, categorizes them (person, decision, event, insight, preference, project), detects entities, and finds relations to existing memories. When a fact updates an existing one, the old memory is marked superseded.
Search
supermemory search "project deadline" --top-k 10
supermemory search "project deadline" --all # include superseded
supermemory search "project deadline" --as-of 2026-03-01 # time travelEntity operations
supermemory stats # counts, categories
supermemory history Sarah # version timeline
supermemory profile Sarah # auto-built entity profileAPI
GET /api/health # status + memory count
POST /api/search # {"query": "...", "top_k": 10}
POST /api/ingest # {"text": "...", "session_id": "..."}
GET /api/entities # all known entities
GET /api/entity/{name} # entity memories + profile
POST /api/search_entities # entity-aware cross-session search
POST /api/aggregate # count/sum queries over event clustersSearch latency: ~32ms warm, ~8s cold start (embedding model load).
Agent integration
Recall at session start
Inject relevant context before the agent processes a message:
supermemory search "current projects and priorities" --top-k 5Auto-ingest from responses
After meaningful agent turns, extract and store facts:
supermemory ingest "$RESPONSE_TEXT" --session $SESSION --agent $AGENT_IDOpenClaw plugin (zero-config)
Install the supermemory-claw plugin for automatic memory injection and extraction with no agent code changes.
Architecture
- Storage: SQLite with WAL mode (concurrent reads, single writer)
- Embeddings: Local sentence-transformers (free, on-device) or API (OpenAI/Cohere/Voyage via litellm)
- Extraction: LLM-based atomic fact extraction with relation detection (default: Haiku)
- Entity system: Join tables, aliases, auto-merged profiles across sources
- Multi-agent: Single DB with agent_id tagging, cross-agent semantic search
Cost
~$0.01-0.02 per ingest (3 LLM calls: extract, relate, profile). Search is free (local embeddings).